Automatic extraction of multilingual dictionary items from non-parallel, multilingual, semi-structured data

ABSTRACT

User interfaces receive a first plurality of user queries and result sets that are in a category, exhibit a constraint, and exhibit user behavior. Also received are a second plurality of user queries and result sets that that are in the category, exhibit the constraint, and exhibit user behavior. The second user queries and results are received either from a plurality of user interfaces coupled to a second system, or from the second system itself. Responsive to detecting that the first plurality of user queries and result sets and the second plurality of user queries and result sets satisfy respective thresholds, a signal indicates that at least one of the first plurality of user queries and at least one of the second plurality of user queries are translations of each other.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright eBay Inc. 2013, All Rights Reserved.

TECHNICAL FIELD

The present application relates generally to machine translation.

BACKGROUND

The use of mobile devices, such as cellphones, smartphones, tablets, andlaptop computers, has increased rapidly in recent years, which, alongwith the rise in dominance of the Internet as the primary mechanism forcommunication, has caused an explosion in international communicationsand/or transactions, including electronic commerce (“ecommerce”). Asthese factors spread throughout the world, communications between usersthat utilize different spoken or written languages increaseexponentially. International transactions pose unique challenges whendealing with differing languages because such transactions often requirespecific information to be highly accurate. For example, if a potentialbuyer asks a seller about some aspect of a product for sale, the answershould be precise and accurate. Any failing in the accuracy of theanswer could result in a lost sale or an unhappy purchaser.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 is a network diagram depicting a client-server system, withinwhich one example embodiment may be deployed.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications and that, in one example embodiment, are provided as partof application server(s) in the networked system.

FIG. 2A is a block diagram illustrating an example machine translationapplication according to an example embodiment.

FIG. 3 is a block diagram illustrating two systems for automaticextraction of multilingual dictionary items from non-parallel,multilingual, semi-structured data according to an example embodiment.

FIG. 4 is a flowchart illustrating an example method, consistent withvarious embodiments.

FIG. 5 is a block diagram illustrating a mobile device, according to anexample embodiment.

FIG. 6 is a block diagram of a machine in the example form of a computersystem within which instructions may be executed for causing the machineto perform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

Example methods and systems for machine translation (MT) forinternational communications and/or transactions are provided. It willbe evident, however, to one of ordinary skill in the art that thepresent inventive subject matter may be practiced without these specificdetails.

Machine translation is a subfield of computational linguistics thatinvestigates the use of software to translate text or speech from onenatural language to another. Machine translation relies heavily onparallel data, often called parallel corpora, for example, translateddocuments, but acquiring parallel corpora is a very time consuming andexpensive, particularly domain specific corpora like ecommerce systemdata. Automatic multilingual dictionaries may be learned from the use ofstructured data that a commercial service (e.g., ecommerce site likethat operated by eBay Inc.) has in its databases. A working assumptionmay be that consumer behavior can be modeled in different countries, anda mathematical model can be found to correlate user interest. With this,specific aspects, categories and images of products or services can beused to constrain the probabilities to identify units that aresemantically similar, across languages. The information may be codedindependently in taxonomies for different languages. Taxonomies, in thiscontext, can be seen as a tree of information, and certain nodes ofthese trees can be aligned by using prior knowledge, even if that priorknowledge is limited, and with machine learning the nodes that are leftout can be assumed to be aligned as well, and the probability of thisalignment can be estimated.

FIG. 1 is a network diagram depicting a client-server system 100, withinwhich one example embodiment may be deployed. A networked system 102, inthe example forms of a network-based marketplace or publication system,provides server-side functionality, via a network 104 (e.g., theInternet or a Wide Area Network (WAN)), to one or more clients. FIG. 1illustrates, for example, a web client 106 (e.g., a browser, such as theInternet Explorer browser developed by Microsoft Corporation of Redmond,Wash. State) and a programmatic client 108 executing on respectivedevices 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more marketplace applications 120 and payment applications122. The application servers 118 are, in turn, shown to be coupled toone or more database servers 124 that facilitate access to one or moredatabases 126.

The marketplace applications 120 may provide a number of marketplacefunctions and services to users who access the networked system 102. Thepayment applications 122 may likewise provide a number of paymentservices and functions to users. The payment applications 122 may allowusers to accumulate value (e.g., in a commercial currency, such as theU.S. dollar, or a proprietary currency, such as “points”) in accounts,and then later to redeem the accumulated value for products (e.g., goodsor services) that are made available via the marketplace applications120. While the marketplace and payment applications 120 and 122 areshown in FIG. 1 to both form part of the networked system 102, it willbe appreciated that, in alternative embodiments, the paymentapplications 122 may form part of a payment service that is separate anddistinct from the networked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the embodiments are, of course, not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousmarketplace and payment applications 120 and 122 could also beimplemented as standalone software programs, which do not necessarilyhave networking capabilities.

The web client 106 accesses the various marketplace and paymentapplications 120 and 122 via the web interface supported by the webserver 116. Similarly, the programmatic client 108 accesses the variousservices and functions provided by the marketplace and paymentapplications 120 and 122 via the programmatic interface provided by theAPI server 114. The programmatic client 108 may, for example, be aseller application (e.g., the TurboLister application developed by eBayInc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 108and the networked system 102.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more promotional,marketplace, or payment functions that are supported by the relevantapplications of the networked system 102.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications 120 and 122 that, in one example embodiment, are providedas part of application server(s) 118 in the networked system 102. Theapplications 120 and 122 may be hosted on dedicated or shared servermachines (not shown) that are communicatively coupled to enablecommunications between server machines. The applications 120 and 122themselves are communicatively coupled (e.g., via appropriateinterfaces) to each other and to various data sources, so as to allowinformation to be passed between the applications 120 and 122 or so asto allow the applications 120 and 122 to share and access common data.The applications 120 and 122 may furthermore access one or moredatabases 126 via the database servers 124.

The networked system 102 may provide a number of publishing, listing,and price-setting mechanisms whereby a seller may list (or publishinformation concerning) goods or services for sale, a buyer can expressinterest in or indicate a desire to purchase such goods or services, anda price can be set for a transaction pertaining to the goods orservices. To this end, the marketplace and payment applications 120 and122 are shown to include at least one publication application 200 andone or more auction applications 202, which support auction-formatlisting and price setting mechanisms (e.g., English, Dutch, Vickrey,Chinese, Double, Reverse auctions, etc.). The various auctionapplications 202 may also provide a number of features in support ofsuch auction-format listings, such as a reserve price feature whereby aseller may specify a reserve price in connection with a listing and aproxy-bidding feature whereby a bidder may invoke automated proxybidding.

A number of fixed-price applications 204 support fixed-price listingformats (e.g., the traditional classified advertisement-type listing ora catalogue listing) and buyout-type listings. Specifically, buyout-typelistings (e.g., including the Buy-ft-Now (BIN) technology developed byeBay Inc., of San Jose, Calif.) may be offered in conjunction withauction-format listings, and allow a buyer to purchase goods orservices, which are also being offered for sale via an auction, for afixed-price that is typically higher than the starting price of theauction.

Store applications 206 allow a seller to group listings within a“virtual” store, which may be branded and otherwise personalized by andfor the seller. Such a virtual store may also offer promotions,incentives, and features that are specific and personalized to arelevant seller.

Reputation applications 208 allow users who transact, utilizing thenetworked system 102, to establish, build, and maintain reputations,which may be made available and published to potential trading partners.Consider that where, for example, the networked system 102 supportsperson-to-person trading, users may otherwise have no history or otherreference information whereby the trustworthiness and credibility ofpotential trading partners may be assessed. The reputation applications208 allow a user (for example, through feedback provided by othertransaction partners) to establish a reputation within the networkedsystem 102 over time. Other potential trading partners may thenreference such a reputation for the purposes of assessing credibilityand trustworthiness.

Personalization applications 210 allow users of the networked system 102to personalize various aspects of their interactions with the networkedsystem 102. For example a user may, utilizing an appropriatepersonalization application 210, create a personalized reference page atwhich information regarding transactions to which the user is (or hasbeen) a party may be viewed. Further, a personalization application 210may enable a user to personalize listings and other aspects of theirinteractions with the networked system 102 and other parties.

The networked system 102 may support a number of marketplaces that arecustomized, for example, for specific geographic regions. A version ofthe networked system 102 may be customized for the United Kingdom,whereas another version of the networked system 102 may be customizedfor the United States. Each of these versions may operate as anindependent marketplace or may be customized (or internationalized)presentations of a common underlying marketplace. The networked system102 may accordingly include a number of internationalizationapplications 212 that customize information (and/or the presentation ofinformation by the networked system 102) according to predeterminedcriteria (e.g., geographic, demographic or marketplace criteria). Forexample, the internationalization applications 212 may be used tosupport the customization of information for a number of regionalwebsites that are operated by the networked system 102 and that areaccessible via respective web servers 116.

Navigation of the networked system 102 may be facilitated by one or morenavigation applications 214. For example, a search application (as anexample of a navigation application 214) may enable key word searches oflistings published via the networked system 102. A browse applicationmay allow users to browse various category, catalogue, or inventory datastructures according to which listings may be classified within thenetworked system 102. Various other navigation applications 214 may beprovided to supplement the search and browsing applications.

In order to make listings available via the networked system 102 asvisually informing and attractive as possible, the applications 120 and122 may include one or more imaging applications 216, which users mayutilize to upload images for inclusion within listings. An imagingapplication 216 also operates to incorporate images within viewedlistings. The imaging applications 216 may also support one or morepromotional features, such as image galleries that are presented topotential buyers. For example, sellers may pay an additional fee to havean image included within a gallery of images for promoted items.

Listing creation applications 218 allow sellers to conveniently authorlistings pertaining to goods or services that they wish to transact viathe networked system 102, and listing management applications 220 allowsellers to manage such listings. Specifically, where a particular sellerhas authored and/or published a large number of listings, the managementof such listings may present a challenge. The listing managementapplications 220 provide a number of features (e.g., auto-relisting,inventory level monitors, etc.) to assist the seller in managing suchlistings. One or more post-listing management applications 222 alsoassist sellers with a number of activities that typically occurpost-listing. For example, upon completion of an auction facilitated byone or more auction applications 202, a seller may wish to leavefeedback regarding a particular buyer. To this end, a post-listingmanagement application 222 may provide an interface to one or morereputation applications 208, so as to allow the seller conveniently toprovide feedback regarding multiple buyers to the reputationapplications 208.

Dispute resolution applications 224 provide mechanisms whereby disputesarising between transacting parties may be resolved. For example, thedispute resolution applications 224 may provide guided procedureswhereby the parties are guided through a number of steps in an attemptto settle a dispute. In the event that the dispute cannot be settled viathe guided procedures, the dispute may be escalated to a third partymediator or arbitrator.

A number of fraud prevention applications 226 implement fraud detectionand prevention mechanisms to reduce the occurrence of fraud within thenetworked system 102.

Messaging applications 228 are responsible for the generation anddelivery of messages to users of the networked system 102 (such as, forexample, messages advising users regarding the status of listings at thenetworked system 102 (e.g., providing “outbid” notices to bidders duringan auction process or to provide promotional and merchandisinginformation to users)). Respective messaging applications 228 mayutilize any one of a number of message delivery networks and platformsto deliver messages to users. For example, messaging applications 228may deliver electronic mail (e-mail), instant message (IM), ShortMessage Service (SMS), text, facsimile, or voice (e.g., Voice over IP(VoIP)) messages via the wired (e.g., the Internet), plain old telephoneservice (POTS), or wireless (e.g., mobile, cellular, WiFi, WiMAX)networks 104.

Merchandising applications 230 support various merchandising functionsthat are made available to sellers to enable sellers to increase salesvia the networked system 102. The merchandising applications 230 alsooperate the various merchandising features that may be invoked bysellers, and may monitor and track the success of merchandisingstrategies employed by sellers.

The networked system 102 itself, or one or more parties that transactvia the networked system 102, may operate loyalty programs that aresupported by one or more loyalty/promotions applications 232. Forexample, a buyer may earn loyalty or promotion points for eachtransaction established and/or concluded with a particular seller, andbe offered a reward for which accumulated loyalty points can beredeemed.

A machine translation application 234 may translate a query in a firstlanguage to a query in a second language, obtain and build an ontologybased on keywords of the query in the first language combined with userfeedback indicating the relevancy of a result set obtained by the querythat is translated into the second language. This ontology is defined bythe users of the first language and may be developed from measuring therelevancy of the result set. A more detailed view of a machinetranslation application in accordance with an embodiment is seen in FIG.2A.

FIG. 2A is a block diagram illustrating an example machine translationapplications 234, according to an example embodiment. The machinetranslation applications 234 comprise query receive module 236, userbehavior module 238, learning and correlation module 240, andtranslation optimizing module 242. Query receive module 236 may receiveuser queries and result sets as more fully discussed with respect, toboth system 320 and system 360 in FIG. 3. User behavior module 238 maybe used to determine whether user behavior has indicated that a userquery is satisfied by a user result set, also as more fully discussedwith respect to system 320 and system 360 in FIG. 3. Learning andcorrelation module 240 may be used in learning how to translate queriesfrom one language to another language based on the assumption ofparallel user behavior in different countries. Translation optimizingmodule 242 may be may be used to optimize translation of queries in thefirst language to queries in the second language.

Certain commercial service providers (e.g., eBay Inc.) may operate andprovide transaction support technology in a number of countries. Forthis discussion a Spain site and a US site are used as examples. Each ofthe two sites may be distinct and have a different inventory of productsand services. Each site hosts massive numbers of user sessions asdescribed in U.S. patent application Ser. No. 14/194,582 entitledIMPROVEMENT OF AUTOMATIC MACHINE TRANSLATION USING USER FEEDBACK, filedon the same date as the filing date as this application, andincorporated herein by reference in its entirety. Each site can draw onthe queries in the user sessions as described in the foregoing patentapplication. An assumption is made that users at the Spain site, whoenter queries in Spanish, and users at the US site, who enter queries inEnglish, act similarly when looking for certain products (e.g., they mayseek the same features for the product). Further, some of the inventoryat the two sites is the same, in that the same product may be offered indifferent countries. This then allows the assumption to be made that themost probable queries at the Spain site and the most probable queries atthe US site have some correlation and, with some probability, may betranslations of each other, or parallel data. Also, the queries mayexhibit a particular user behavior. In one embodiment the user behaviourmay be clicking on an item in a result set, which may be viewed as apositive user behavior. The positive behavior to a translation of anitem description may be characterized by clicking “positive” links onthe website, e.g., act towards reading further details on the respectiveitem, or, for example, clicking on the “bid”, “buy it now”, “watch” orsimilar activity. In one embodiment, the most clicked-on items in theresult set for a query at the Spain site and the most clicked-on itemsin the result set for a query at the US site may be considered to havesome correlation as well. If the products are constrained (e.g., comefrom the same category, and have similar aspects), the assumption may bemade that the data in the queries are parallel data in a machinetranslation sense.

Potentially “negative” signals are considered as well. Negative signalsmay be if the user does NOT click on the positive links and/or cancelstheir action of the current research. A more “negative” signal is if theuser clicks on a button, which may appear in the user interface, to turnoff machine translation.

The foregoing behaviors may be considered implicit positive signals andimplicit negative signals. There may also be explicit “positive” signalsand explicit “negative” signals. An explicit positive signal may be auser sending a mark, for example a star rating, that indicates that theresult set was helpful. An explicit negative signal may be if the userprovides a mark, for example a star rating, with a user rating that thetranslation is not helping and/or is misleading. Other user behavior maybe potentially negative. For example, a mid-range star rating, forexample 3 out of 5, may be viewed as potentially negative.

Both positive and negative user behaviors (including positive,potentially negative and clearly negative ones) may be used in improvingthe machine translation. Good translations (evoking a positive userbehavior) will be statistically rewarded, and more negative translations(evoking a potentially negative and/or a clearly negative userbehaviour) will be statistically punished. This scoring will be used onthe units/entries/“events” in statistical models that lead to a specificmachine translation output.

Consequently since the data discussed above, under the recitedconditions, or constraints, can be considered parallel data, the systemmay not need to look for information from a product vendor or from userfeedback or the like, for machine translation. Instead, the system canextract parallel data from the system's own databases, given that largetransaction processing systems may host millions of user sessions dailyand can monitor user sessions in nearly real time, as discussed in theabove patent application. It follows that the above behavior model mayaid the system in learning how to translate queries from one language toanother language based on the above assumption of parallel user behaviorin different countries. In one embodiment, if a user at the US site islooking for an iPhone with certain features, the probability that a userat the Spain site, when looking for an iPhone will seek similar featuresfor the iPhone, may be high. This is a constraint that may allow findingthe best, or very good, translations even without expecting either userto tell the system, by feedback or otherwise, that one query is atranslation of another query. In other words, user behavior may be thealigning context between queries made in the different countries and indifferent languages.

Assuming that a user at the US site queries for a device in the categoryof electronics at the US site and the device in the result set is aniPhone, and the user clicks on the iPhone, if a user performs the samequery at the Spain site, and it is the same category, namelyelectronics, the system may understand in Spanish without anytranslation, that the query is for an iPhone in the result set. If theuser at the Spain site then clicks on the iPhone in the result set,there is a calculable probability that the query at the Spain site andthe query at the US site are the same. The system would then learn fromdefining the category, some restraining context such as aspects, theresult set, and the action taken on the result set, in differentcountries, both queries, that at the Spain site and that at the US site,have a correlation and may be translations of each other. The constraintneed not be a brand, as in the case of an iPhone, which carries theAPPLE™ brand. The constraint can be any feature of the queried product.For example, if there is a query for a dress at the Spain site and aquery for a dress at the US site, the constraint may be aspects asdiscussed above, and the system may learn that the two queries may betranslations of each other. The constraints need not be aspects. Theconstraint may be a bar code, which is the same for the product at theSpain site and the product at the US site. Or the constraint may be aseller selling the same product at the two sites. Such constraints mayaid the system in identifying queries that may be translations of eachother, from which the system can learn for future query translations.This can be the case even in new languages the system has never madetranslations for in the past. User behavior implies query similarity asa statistical probability. Further, the more of this type of examplethat the system encounters, the more confidence can be placed in theassumption that the behavior model indicates that queries in differentlanguages may be translations of each other.

FIG. 3 is a block diagram illustrating two systems for automaticextraction of multilingual dictionary items from non-parallel,multilingual, semi-structured data, according to an example embodiment.Publication system 300, which may be a publication system (e.g., such aseBay Spain), comprises user interface 310, which may be similar tomachines 110, 112 or 130 of FIG. 1. User interface 310 is coupled to thepublication system such as eBay Spain at 320, which may include MTapplication 234′ which may be the same or similar to machine translationapplication 234 of FIG. 2. System 320 may be similar to system 102 ofFIG. 1. System 320 may be coupled to, or may include, database 330.

Publication system 301 includes components that may be the same as, orsimilar to, those of publication system 300. User interface 350 iscoupled to system 360, which may include MT application 234. System 360may be coupled to, or may include, database 370. Line (or bus) 352 thatcouples user interface 350 to system 360 is also coupled to MTapplication 234′ of system 320. While interfaces 310 and 350 areillustrated as a single interface, each of the boxes 310 and 350 maycomprise a plurality of user interfaces. Line 352 transmits user queriesas well as user listing selections from result sets from user interface350 to both system 360 and to the MT application 234′ of system 320.Line 312 functions similarly with respect to systems 320 and MTapplication 234 of system 360. Similarly, line (or bus) 312 that couplesuser interface 310 to system 320 is also coupled to MT application 234of system 360. Line 352 functions similarly to line 312, but withrespect to system 360 and MT application 234′ of system 320. In the caseof each system 300 and 301, user queries, and user selections fromresult sets, may be respectively transmitted to the MT application ofthe opposite system, among other functions performed by systems 300 and301. While lines 314 and 354 are illustrated as originating from userinterfaces, 310 and 350, respectively, one of ordinary skill in the artwill readily recognize that they could originate from within systems 320and 360, respectively. If the lines originate from the respective userinterfaces then the receiving system may count the number of clicks andtotal the respective percentages discussed below. If the lines originatefrom the respective systems those systems may perform those functionsbefore transmitting the information to the receiving system.

Because some of the products at the US site and the Spain site are thesame or similar, user behavior with respect to products at either site,with appropriate constraints, can be used to align queries in the twodifferent languages for machine translation purposes. If the US site 301determines, for example via MT application 234, that there are 100 usersat the Spain site that enter a query for a product, with a constraint asdiscussed above, and obtain result sets that result in a successfuloutcome (e.g., the user clicks on the result set) for, in oneembodiment, 30% of the queries, then, depending on a thresholdprobability the system may set, the Spanish language query may becorrelated with an English language query for a product, with a similarconstraint. Similarly, if the Spain site 300 determines, for example viaMT application 234′, that there are 1,000 users at the US site thatenter a product query with the above constraint and obtain result setsthat result in a positive outcome (e.g. the user clicks on the resultset) for, in one embodiment, 25% of the queries, then, depending on athreshold probability the system may set, the English language query maybe correlated with a Spanish language query for the product with asimilar constraint. These scenarios are seen by the dash line connectingdatabase 330 and database 370 of FIG. 3. The above percentages may beviewed as a threshold which may be set at any level that may be deemedappropriate to indicate that the percentages are statisticallysignificant. Again, as discussed above, the constraint may be aspects, abar code which is the same for the product at the Spain site and theproduct at the US site, or a seller selling the same product at the twosites, among other things. Because the systems 300 and 301 know, via MTapplications 234′ and 234, the kind of process the users went through inthe two countries, and these products may be correlated—iPhone, black,etc., it follows that the systems 300 and 301 can correlate (or indicateto be parallel data) what the user sees at the Spain site and what theuser sees at the US site with a certain probability.

FIG. 4 is a flowchart illustrating an example method 400, consistentwith various embodiments. The vertical leg beginning with 404 describesactivities at a second system after receiving, at 402, user queries andresult sets from a first system. The vertical leg beginning with 414describes activities at a first system, after receiving, at 412 userqueries and result sets from a second system. The first system may beeBay US 360 in FIG. 3 and the second system may be eBay Spain 320 inFIG. 3. At 402 the first system sends user queries that are in a givencategory and that exhibit a constraint, as explained in FIG. 3, andresult sets that correspond to the user queries, which are received at404 by the second system. The receiving may be accomplished by queryreceive module 236 of FIG. 2A. At 406 the second system tests todetermine whether the received query is satisfied by the result set, forexample by a user behavior such as clicking on a product in the resultset. This may be accomplished by user behavior module 238 of FIG. 2A. Ifthe NO branch is taken, the system waits for the next user query andresult set before making the test on that new user query and result set.If the YES branch is taken, the number of user queries that aresatisfied by their result sets is increased by one at 408.

At 410 a test is made to determine whether the threshold discussed inFIG. 3 has been met. If the NO branch is taken the system awaits thenext increase to the number of user queries that are satisfied by theirresult sets at 410 before making the next test to determine whether thethreshold is met. If the YES branch is taken at 410, then the thresholdset by the system has been met and the second system sends a signal todecision block 422 which may be continually testing to determine whetherthe respective thresholds of the first system and the second system havebeen met. The vertical leg beginning at 412 operates as described forthe vertical leg that begins at 402, except that the activity is takenat the first system after the second system sends user queries that arein a given category and that exhibit a constraint, as explained in FIG.3, and result sets that correspond to the user queries, which arereceived at 414 by the second system. The rest of the vertical 412 legoperates as explained for the vertical 402 leg. When the threshold ismet for the second system as indicated by the YES branch at decision420, a signal is sent to decision block 422 which, as mentioned above,may be continually testing to determine whether the respectivethresholds of both the first system and the second system have been met.When signals from the YES branch of 410 and from the YES branch of 420reach decision 422, the YES branch is taken at 422 and the queries fromthe first system and the queries from the second system may be indicatedto be translations of each other as at 424. The frequency of goingthrough the YES branch versus the NO branch of 410 and 420 might supportin calculating the confidence of the translation candidates. This isindicated symbolically at lines 411 and 421 where going through the YESand NO branches may be used in calculation (not shown).

Example Mobile Device

FIG. 5 is a block diagram illustrating a mobile device 500, according toan example embodiment. The mobile device 500 may include a processor502. The processor 502 may be any of a variety of different types ofcommercially available processors suitable for mobile devices (forexample, an XScale architecture microprocessor, a microprocessor withoutinterlocked pipeline stages (MIPS) architecture processor, or anothertype of processor 502). A memory 504, such as a random access memory(RAM), a flash memory, or other type of memory, is typically accessibleto the processor 502. The memory 504 may be adapted to store anoperating system (OS) 506, as well as application programs 508, such asa mobile location enabled application that may provide LBSs to a user.The processor 502 may be coupled, either directly or via appropriateintermediary hardware, to a display 510 and to one or more input/output(I/O) devices 512, such as a keypad, a touch panel sensor, a microphone,and the like. Similarly, in some embodiments, the processor 502 may becoupled to a transceiver 514 that interfaces with an antenna 516. Thetransceiver 514 may be configured to both transmit and receive cellularnetwork signals, wireless data signals, or other types of signals viathe antenna 516, depending on the nature of the mobile device 500.Further, in some configurations, a GPS receiver 518 may also make use ofthe antenna 516 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on machine-readable storage or(2) in a transmission signal) or hardware-implemented modules. Ahardware-implemented module is a tangible unit capable of performingcertain operations and may be configured or arranged in a certainmanner. In example embodiments, one or more computer systems (e.g., astandalone, client or server computer system) or one or more processors502 may be configured by software (e.g., an application or applicationportion) as a hardware-implemented module that operates to performcertain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure processor 502, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses thatconnect the hardware-implemented modules). In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 502 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 502 may constitute processor-implementedmodules that operate to perform one or more operations or functions. Themodules referred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors 502 orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors 502, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor 502 or processors502 may be located in a single location (e.g., within a homeenvironment, an office environment or as a server farm), while in otherembodiments the processors 502 may be distributed across a number oflocations.

The one or more processors 502 may also operate to support performanceof the relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of data processing apparatus, e.g., a programmable processor502, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors 502 executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures meritconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor 502), or acombination of permanently and temporarily configured hardware may be adesign choice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 6 is a block diagram of machine in the example form of a computersystem 600 within which instructions 624 may be executed for causing themachine to perform any one or more of the methodologies discussedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a personal digital assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 600 includes a processor 602 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 604 and a static memory 606, which communicate witheach other via a bus 608. The computer system 600 may further include avideo display unit 610 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 600 also includes analphanumeric input device 612 (e.g., a keyboard or a touch-sensitivedisplay screen), a user interface (UI) navigation (e.g., cursor control)device 614 (e.g., a mouse), a disk drive unit 616, a signal generationdevice 618 (e.g., a speaker) and a network interface device 620.

Machine-Readable Medium

The disk drive unit 616 includes a computer-readable medium 622, whichmay be hardware storage, on which is stored one or more sets of datastructures and instructions 624 (e.g., software) embodying or utilizedby any one or more of the methodologies or functions described herein.The instructions 624 may also reside, completely or at least partially,within the main memory 604 and/or within the processor 602 duringexecution thereof by the computer system 600, the main memory 604 andthe processor 602 also constituting computer-readable media 622.

While the computer-readable medium 622 is shown in an example embodimentto be a single medium, the term “computer-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 624 or data structures. The term “computer-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions 624 for executionby the machine and that cause the machine to perform any one or more ofthe methodologies of the present disclosure or that is capable ofstoring, encoding or carrying data structures utilized by or associatedwith such instructions 624. The term “computer-readable medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, and optical and magnetic media. Specific examples ofcomputer-readable media 622 include non-volatile memory, including byway of example semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 624 may further be transmitted or received over acommunications network 626 using a transmission medium. The instructions624 may be transmitted using the network interface device 620 and anyone of a number of well-known transfer protocols (e.g., HTTP). Examplesof communication networks include a local area network (“LAN”), a widearea network (“WAN”), the Internet, mobile telephone networks, plain oldtelephone (POTS) networks, and wireless data networks (e.g., WiFi andWiMax networks). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions 724 for execution by the machine, and includesdigital or analog communications signals or other intangible media tofacilitate communication of such software.

Although the inventive subject matter has been described with referenceto specific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the disclosure.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense. The accompanying drawingsthat form a part hereof, show by way of illustration, and not oflimitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDetailed Description, therefore, is not to be taken in a limiting sense,and the scope of various embodiments is defined only by the appendedclaims, along with the full range of equivalents to which such claimsare entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The invention claimed is:
 1. A method of machine translation comprising:receiving at a second publication system, from a first publicationsystem, a first plurality of user queries that are in a first language,that are in a category and that exhibit a constraint, and a firstplurality of result sets, respective ones of which correspond torespective ones of the first plurality of user queries and that exhibita first group of implicit user behavior with respect to the firstplurality of user result sets; receiving, at the first publicationsystem, from the second publication system, a second plurality of userqueries that are in a second language, that are in the category and thatexhibit the constraint, and a second plurality of result sets,respective ones of which correspond to respective ones of the secondplurality of user queries and that exhibit a second group of implicituser behavior with respect to the second plurality of result sets;detecting, by a user behavior module of a machine translationapplication in the second publication system, implicit user behavior inthe first group of implicit user behavior that indicates that at leastsome of the first plurality of user queries are satisfied by respectiveones of the first plurality of result sets; detecting, by a userbehavior module of a machine translation application in the firstpublication system, implicit user behavior in the second group ofimplicit user behavior that indicates that at least some of the secondplurality of user queries are satisfied by respective ones of the secondplurality of result sets; detecting that the at least some of the firstplurality of user queries and the at least some of the second pluralityof user queries satisfy respective thresholds; and responsive todetecting that the at least some of the first plurality of first userqueries and the at least some of the second plurality of user queriessatisfy respective thresholds, providing an indication to at least oneof the machine translation application in the second publication systemor the machine translation application in the first publication systemthat at least one of the first plurality of user queries and at leastone of the second plurality of user queries are likely to betranslations of each other.
 2. The method of claim 1 wherein theconstraint comprises one of aspects, a bar code or a seller.
 3. Themethod of claim 1 wherein the first group of implicit user behavior andthe second group of implicit user behavior comprise one of positivebehavior, potentially negative behavior or clearly negative behavior. 4.The method of claim 1 wherein the respective thresholds comprise a firstpercentage of the plurality of the first user queries that is satisfiedby result sets responsive to the plurality of first user queries, and asecond percentage of the second plurality of user queries that issatisfied by result sets responsive to the plurality of second userqueries, and the satisfaction is based on one of positive user behavior,potentially negative user behavior, or clearly negative user behavior.5. One or more computer-readable hardware storage device having embeddedtherein a set of instructions which, when executed by one or moreprocessors of a computer, causes the computer to execute operationscomprising: receiving at a second publication system, from a firstpublication system, a first plurality of user queries that are in afirst language, that are in a category and that exhibit a constraint,and a first plurality of result sets, respective ones of whichcorrespond to respective ones of the first plurality of user queries andthat exhibit a first group of implicit user behavior with respect to thefirst plurality of user result sets; receiving, at the first publicationsystem, from the second publication system, a second plurality of userqueries that are in a second language, that are in the category and thatexhibit the constraint, and a second plurality of result sets,respective ones of which correspond to respective ones of the secondplurality of user queries and that exhibit a second group of implicituser behavior with respect to the second plurality of result sets;detecting, by a user behavior module of a machine translationapplication in the second publication system, implicit user behavior inthe first group of implicit user behavior that indicates that at leastsome of the first plurality of user queries are satisfied by respectiveones of the first plurality of result sets; detecting, by a userbehavior module of a machine translation application in the firstpublication system, implicit user behavior in the second group ofimplicit user behavior that indicates that at least some of the secondplurality of user queries are satisfied by respective ones of the secondplurality of result sets; detecting that the at least some of the firstplurality of user queries and the at least some of the second pluralityof user queries satisfy respective thresholds; and responsive todetecting that the at least some of the first plurality of first userqueries and the at least some of the second plurality of user queriessatisfy respective thresholds, providing an indication to at least oneof the machine translation application in the second publication systemor the machine translation application in the first publication systemthat at least one of the first plurality of user queries and at leastone of the second plurality of user queries are likely to betranslations of each other.
 6. The one or more computer-readablehardware storage device of claim 5 wherein the constraint comprises oneof aspects, a bar code or a seller.
 7. The one or more computer-readablehardware storage device of claim 5 wherein the first group of implicituser behavior and the second group of implicit user behavior compriseone of positive behavior, potentially negative behavior or clearlynegative behavior.
 8. The one or more computer-readable hardware storagedevice of claim 5 wherein the respective thresholds comprise a firstpercentage of the plurality of the first user queries that are satisfiedby result sets responsive to the plurality of first user queries and asecond percentage of the second plurality of user queries that aresatisfied by result sets responsive to the plurality of second userqueries, and the satisfaction is based on one of positive user behavior,potentially negative user behavior, or clearly negative user behavior.9. A system comprising: one or more computer processors and storage, ata second publication system, configured to: receive, from a firstpublication system, a first plurality of user queries that are in afirst language that are in a category and that exhibit a constraint, anda first plurality of result sets, respective ones of which correspond torespective ones of the first plurality of user queries and that exhibita first group of implicit user behavior with respect to the firstplurality of user result sets; detect, by a user behavior module of amachine translation application at the second publication system,implicit user behavior in the first group of implicit user behavior thatindicates that at least some of the first plurality of user queries aresatisfied by respective ones of the first plurality result sets; one ormore computer processors and storage, at the first publication system,configured to: receive, from the second publication system, a secondplurality of user queries in a second language that that are in thecategory and that exhibit the constraint, and a second plurality ofresult sets that exhibit a second group of implicit user behavior withrespect to the second plurality of result sets; detect by a userbehavior module of a machine translation application at the firstpublication system, implicit user behavior in the second group ofimplicit user behavior that indicates that at least some of the secondplurality of user queries are satisfied by respective ones of the secondplurality of result sets; execute a decision application configured todetect that the at least some of the first plurality of user queries andthe at least some of the second plurality of user queries satisfyrespective thresholds; and execute a learning and correlation moduleconfigured, responsive to the detection that the at least some of thefirst plurality of first user queries and the at least some of thesecond plurality of user queries satisfy respective thresholds, toprovide an indication to at least one of the machine translationapplication in the second publication system or the machine translationapplication in the first publication system that at least one of thefirst plurality of user queries and at least one of the second pluralityof user queries are likely to be translations of each other.
 10. Thesystem of claim 9 wherein the constraint comprises one of aspects, a barcode or a seller.
 11. The system of claim 9 wherein the first group ofimplicit user behavior and the second group of implicit user behaviorcomprises one of positive behavior, potentially negative behavior orclearly negative behavior.
 12. The system of claim 9 wherein therespective thresholds comprise a first percentage of the plurality ofthe first user queries that are satisfied by result sets responsive tothe plurality of first user queries and a second percentage of thesecond plurality of user queries that are satisfied by result setsresponsive to the second plurality of user queries.